Sensitivity analysis is often used to judge the sensitivity of model behaviour to
uncertain assumptions about model formulations and parameter values. Since the
ultimate goal of modelling is typically policy recommendation, one may suspect that it
is even more useful to test the sensitivity of policy recommendations. A major reason
for this is that behaviour sensitivity is not necessarily a reliable predictor of policy
sensitivity. Policy sensitivity analysis is greatly simplified if one can find optimal
policies. Then one can simply see how the optimal policy changes when the model
assumptions are altered. Our case is a fishery model. We investigate how (near-to)
optimal policies change when we correct for a typical estimation bias in an aggregate
model, when we substitute the aggregate model with a cohort representation of the
same fishery, and when we switch from assuming variable to assuming constant fish
prices and per unit variable costs. Normally these assumptions follow from the
analyst’s school of thought without testing. The most surprising result is that while
assumptions about the fish price and the per unit variable costs matter a lot, the choice
between an aggregate and a cohort model is of little importance.